This workshop will focus on the synergies between analysis results from the PharmacoGx, Xeva and RadioGx packages and their usefulness for discovery of biomarkers of drug and/or radiation sensitivity in cancer cell lines (CCLs) and patent derived xenograft models (PDXs). We will discuss issues with data curation, consistency and reproducibility within the literature as well as illustrate the importance of unified analytical platforms, data and code sharing in bioinformatics and biomedical research. In this lab learners will be led through an analysis for each of the three packages on data provided by the download functions within them. The results of these analyses will then be explored to highlight how drug and radiation dose-response profiles in CCLs and PDXs can be used to discover potential synergistic biomarkers for drug-radiation and drug combination therapies. The resulting biomarkers will be discussed in the context of translational cancer research and clinical applications of genomic data. We will conclude with a discussion of how these biomarkers can be used to inform future in vitro and in vivo treatment screenings and ultimately provide useful insights for clinical trial design.
List any workshop prerequisites:
List relevant background reading for the workshop, including any theoretical background you expect students to have.
Participants expected to have the following required packages installed on their machines to be able to run the commands along with the instructors.
PharmacoGx, Xeva, RadioGx, CoreGx and Biobase from Bioconductor
The workshop will be presented as a set of analysis steps to be replicated by the learners, with instructors available to explain the why and how of applying these functions to the given datasets. Learners will write analysis scripts as well as use interactive commands to explore the generated data structures and results. Learners will then brainstorm potential applications of the analysis results in their field as well as comment on use case examples presented by the instructors on research in our lab.
Bioconductor:
For a 2-hr workshop:
| Activity | Time |
|---|---|
| Introduction | 5m |
| Data Structure and Common Functions Between Packages | 10m |
| Basic functionalities of PharmacoGx | 10m |
| Basic functionalities of Xeva | 10m |
| Basic functionalities of RadioGx | 10m |
| Data Analysis Walkthrough | 30m |
| Understanding Analysis Results and Synergies | 10m |
| Use Case Discussion: Translational Science | 15m |
This tutorial, titled Biomarker Discovery from High Throughput Screening Datasets, focuses on a suite of packages designed to simplify analysis correlating measures of treatment response measured on model systems with molecular profiles of those systems. These packages primarly focus on modeling response to cancer treatments in cancer models, but the concepts presented can be applied to other disease types, given availability of data for the area of interest.
The first two of the packages, PharmacoGx and RadioGx focus on screens done using cell lines as a model system. PharmacoGx focuses on modeling and predicting the response of cell lines treated with small molecule therapies. RadioGx swaps the small molecule treatment for irradiation of cell lines. The third package, Xeva, looks at treatment response for in vivo systems, primarily Patient Derived Xenographs of cancer tumours growing in immune compromised mice.
While the experiments analyzed by the three packages are different, the workflow that these packages accommodate has many similarities between them. First and foremost, all three packages were developed to provide data structures that package together screening and molecular data, as well as all related metadata, for complete studies. These packages also provide an interface to download large, publically available datasets which have standardized naming conventions, so that data across different studies is immediately comparable.
Furthermore, the analysis workflow shares common tasks across the three domains covered by these packages. This includes understanding and visualizing the data that is present in each dataset; modeling the response of the model systems to treatment; summarizing that response into a measure comparable across models; extracting and summarizing measurements of molecular features; and ultimately correlating the response of model systems to their molecular state.
In this tutorial, we will be highlighting each analysis step across all three packages, and then finish with a demonstration of how to integrate across the different domains presented here.
RadioGx and PharmacoGx are both used to analyze data from in vitro, cell based assays. In cancer, the most common in vitro model used is the cancer cell line. These cell lines are usually obtained from patient tumour biopsies, which are passaged in a dish and often treated with factors to immortalize the cells - that is, to grant them infinite proliferation capacity. Such models are often deposited in repositories such as the ATCC, and made available for purchase to researchers who wish to use them in their experiments.
When studying Pharmacogenomics in vitro, the design of the experiment usually involves doing two things in parallel: Each cell line in the study is profiled on a molecular level, which can include measuring DNA level aberations such as SNVs and copy number changes, RNA expression levels, DNA methylation, and protein expression. In parallel, the cell lines undergo a viability based drug screen, where cell lines are treated with a panel of drugs, over a range of concentrations, and the growth of each cell line is compared to an untreated control.
The variable response to drug treatement across different cell lines is then compared with the molecular states of the cells, using machine learning or statistical approaches to discern which molecular features are predictive of the observed drug response.
Radiogenomics follows a very similar design to Pharmacogenomics. The main difference is the form of the treatment applied to cells - in Pharmacogenomics, small molecule treatments are studied, while in radiogenomics, the treatement is irradiation of the cells. While the design of the study is similar, the difference in treatement causes differences in how the response of cells is modelled and quantified, as we detail below.
Currently, there is only one major, high-throughput dataset looking at in vitro radiogenomics, the Cleveland Clinic dataset. In this dataset, gamma radiation was used as the treatment. Conceptually however, there is no reason why alpha and beta radiation could not be studied in a similar experimental setup.
In vivo models play an important role in cancer drug discovery. Patent derived xenografts (PDXs) are commonly used in vivo models in cancer research. They have several advantages over cell lines: * PDXs are known to recapitulate tumor biology very well * PDXs maintain the cellular and histological structure of the source tumor * PDXs also keep tumor micro-enviroment and stromal elements * Genomic properties of the tumor are largely preserved from patient to PDX * Drug response of PDXs closely match the corresponding patient and clinical outcome
To create PDXs, cancerous tissues or cells from patients’ tumors are engrafted into immunodeficient mice (either subcutaneous or orthotopic engraftment).
Once established, tumors from the PDXs can be passed from mouse to mouse, this is called passaging. For drug testing, PDXs were given a set drug dose at a set schedule (for example weekly). Change in tumor size is measured, usually physically using calipers.
PharmacoGx stores drug screening data together with molecular profiling of
cell lines in an object called a PharmacoSet, or PSet for short. This object
inherits for the CoreSet class defined in our package CoreGx, which abstracts
the common functionality across our similar packages.
The basic structure of a PharmacoSet is as follows:
Figure 1: PharmacoSet class diagram
Objects comprising a PharmacoSet are enclosed in boxes. First box indicates type and name of each object. Second box indicates the structure of an object or class. Third box shows accessor methods from PharmacoGx for that specific object. ‘=>’ represents return and specifies what is returned from that item or method.
Let us download the PSets used for this tutorial into our local working environment. A table of available PharmacoSet objects can be obtained by using the availablePSets function.
library(PharmacoGx)
availablePSets()
If you look through the table produced by availablePSets, you will notice that each PSet object is downloaded from Zenodo - an Open Science project supported by the European Commision and CERN. Each PSet is assigned a DOI, which can be used to uniquely identify the data used in your analysis and retrieve it in the future for research reproducibility.
Any of the PharmacoSets in the table can then be downloaded by calling downloadPSet, which saves the datasets into a directory of the users choice, and returns the data into the R session. Lets try downloading the GDSC and CCLE PSets. The following code is included as an example, but not run, as the objects were preloaded onto the VM for the workshop.
GDSC <- downloadPSet("GDSC_2020(v1-8.2)", saveDir=file.path(".", "PSets"))
CCLE <- downloadPSet("CCLE_2015", saveDir=file.path(".", "PSets"))
To load the datasets used in this workshop, we run the following code:
data(GDSC)
data(CCLE)
We can print out summary information about these datasets to the console, simply by evaluating them on the command line.
CCLE
## Name: CCLE
## Date Created: Mon Apr 27 00:20:19 2020
## Number of cell lines: 1094
## RNA:
## Dim: 10012 1036
## Drug pertubation:
## Please look at pertNumber(cSet) to determine number of experiments for each drug-cell combination.
## Drug sensitivity:
## Number of Experiments: 11670
## Please look at sensNumber(cSet) to determine number of experiments for each drug-cell combination.
The RadioSet has a structure similar to the PharmacoSet and also inherits
from the CoreSet1 exported by CoreGx class . The radiation
slot is implemented in RadioGx to hold relevant metadata
about the type(s) of radiation used in the dose-response experiment,
and is analogous to the drug slot in a PharmacoSet. The remainder of the slots
mirror the PharmacoSet.
Figure 2: RadioSet class diagram
Objects comprising a RadioSet are enclosed in boxes. First box indicates type and name of each object. Second box indicates the structure of an object or class. Third box shows accessor methods from RadioGx for that specific object. ‘=>’ represents return and specifies what is returned from that item or method.
RadioGx provides an interface similar to PharmacoGx and Xeva for downloading our curated versions of published datasets.
To get a list of available RadioSets, use:
RSets <- availableRSets()
| RadioSet_name | Date_updated | DOI |
|---|---|---|
| Cleveland | 2020/06/16 | 10.5281/zenodo.3899555 |
As the RadioGx package was only recently released, there is currently only one dataset available. Let’s download the ‘Cleveland’ RSet, which contains a highly curated version of the data from Yang et al., 2016.
For this workshop, we have subset this dataset. The entire dataset can be downloaded using the following:
Cleveland <- downloadRSet('Cleveland', saveDir='data')
Let’s load the RSet for this workshop.
data(Cleveland)
Cleveland
## Name: Cleveland
## Date Created: Wed Oct 25 17:38:42 2017
## Number of cell lines: 303
## Number of radiation types: 1
## RNA:
## Dim: 20049 292
## RNASeq:
## Dim: 61958 292
## CNV:
## Dim: 24960 250
## Drug pertubation:
## Please look at pertNumber(rSet) to determine number of
## experiments for each radiation-cell combination.
## Drug sensitivity:
## Number of Experiments: 305
## Please look at sensNumber(rSet) to determine number of
## experiments for each radiation-cell combination.
Similar to PharmacoGx and Xeva, a summary of the contents of the RadioSet is printed when calling a RadioSet in the console. We can see that the Cleveland RSet contains sensitivity information for 303 cell-lines treated with a single type of radiation. The RSet also contains rna2 microarray, rna-seq and cnv molecular data for a subset of available cell-lines.
The R package Xeva connects the tumor volume vs. time data with genomics data in an R class called XevaSet. This allows users to analyze the data and perform biomarker discovery.
Several XevaSet objects can be obtained by using the downloadXevaSet
function.
library(Xeva)
Xeva::downloadXevaSet()
## XevaSet.Name
## 1 PDXE_BRCA
## 2 PDXE_CM
## 3 PDXE_CRC
## 4 PDXE_NSCLC
## 5 PDXE_PDAC
## 6 PDXE
## Description
## 1 XevaSet of breast cancer PDXs from the PDXE dataset. Source: Gao et al., Nature Medicine, 2015
## 2 XevaSet of cutaneous melanoma (CM) PDXs from the PDXE. Source: Gao et al., Nature Medicine, 2015
## 3 XevaSet of colorectal cancer (CRC) PDXs from the PDXE. Source: Gao et al., Nature Medicine, 2015
## 4 XevaSet of non-small-cell lung carcinoma (NSCLC) PDXs from the PDXE. Source: Gao et al., Nature Medicine, 2015
## 5 XevaSet of pancreatic ductal carcinoma (PDAC) PDXs from the PDXE. Source: Gao et al., Nature Medicine, 2015
## 6 XevaSet of PDXE dataset containing all tissue type. Source: Gao et al., Nature Medicine 2015
## Available.Molecular.Profiles Date.Updated
## 1 RNASeq,mutation,cnv Fri Mar 15 16:58:41 2019
## 2 RNASeq,mutation,cnv Fri Mar 15 17:00:39 2019
## 3 RNASeq,mutation,cnv Fri Mar 15 17:01:15 2019
## 4 RNASeq,mutation,cnv Fri Mar 15 16:59:51 2019
## 5 RNASeq,mutation,cnv Fri Mar 15 17:02:20 2019
## 6 RNASeq,mutation,cnv Fri Mar 15 16:53:26 2019
## URL
## 1 https://www.pmgenomics.ca/bhklab/sites/default/files/downloads/XevaSets/data/PDXE_BRCA.rds
## 2 https://www.pmgenomics.ca/bhklab/sites/default/files/downloads/XevaSets/data/PDXE_CM.rds
## 3 https://www.pmgenomics.ca/bhklab/sites/default/files/downloads/XevaSets/data/PDXE_CRC.rds
## 4 https://www.pmgenomics.ca/bhklab/sites/default/files/downloads/XevaSets/data/PDXE_NSCLC.rds
## 5 https://www.pmgenomics.ca/bhklab/sites/default/files/downloads/XevaSets/data/PDXE_PDAC.rds
## 6 https://www.pmgenomics.ca/bhklab/sites/default/files/downloads/XevaSets/data/PDXE.rds
The following chunk loads the dataset used in the tutorial and prints out its summary:
data("brca")
brca
## XevaSet
## name: PDXE.BRCA
## Creation date: Fri Sep 14 11:41:33 2018
## Number of models: 849
## Number of drugs: 22
## Moleculer dataset: RNASeq, mutation, cnv
There are many common functions between the datasets for extracting the data relevant to your analysis. PharmacoGx are RadioGx are the most similar, due to the large similarities in their experimental designs. For most of your work with the objects, we recommend you use the accessor functions implemented in the packages to access the data. These accessors are largely stable across versions, while the implementation in the background can change as datasets grow in size and complexity.
This section is meant both as an introduction, and as a reference to the most used accessors for each package.
PharmacoGx and RadioGx share a lot of similarities in their object structure. This also means that a lot of the functions used to access the data provided in these objects can be accessed using the same interface.
In general, the PSet and RSet store three major categories of data: metadata/annotations, molecular data and radiation response data. These are demarcated in Fig. 1 and Fig. 2 using green, blue and red, respectively. Accessor methods are available to retrieve all three kinds of data from a CoreSet; the accessor methods for each component are listed in the bottom most cell of each object in the class diagrams. We will discuss a subset of these methods now.
There are two major metadata slots in the CoreSet which are commonly useful for your analysis.
The first is common between all the packages, the cell slot. This slot contains
information about the cell lines which were profiled in the dataset. This information exists
for all cell lines, if they exist in any of the molecular or sensitivity data included in the
objects. Note that not all cell lines are profiled in all assays, which is one of the sources
of complexity when analyzing data from large studies.
To access the cell line metadata, you can use the following accessors:
knitr::kable(cellInfo(CCLE)[1:3,1:3])
| cellid | link | CCLE.name | |
|---|---|---|---|
| 1321N1 | 1321N1 | http://www.broadinstitute.org/ccle/cell%20lines/1321N1_CENTRAL_NERVOUS_SYSTEM | 1321N1_CENTRAL_NERVOUS_SYSTEM |
| 143B | 143B | http://www.broadinstitute.org/ccle/cell%20lines/143B_BONE | 143B_BONE |
| 22Rv1 | 22Rv1 | http://www.broadinstitute.org/ccle/cell%20lines/22RV1_PROSTATE | 22RV1_PROSTATE |
knitr::kable(cellInfo(Cleveland)[1:3,1:3])
| cellid | tissueid | CellLine | |
|---|---|---|---|
| SK-N-FI | SK-N-FI | autonomic_ganglia | SKNFI |
| SK-N-AS | SK-N-AS | autonomic_ganglia | SKNAS |
| CHP-212 | CHP-212 | autonomic_ganglia | CHP212 |
The standardized identifiers for those cell lines across our packages are
the row names of this object. For convenience, they are accessible through
the cellNames function:
head(cellNames(CCLE))
## [1] "1321N1" "143B" "22Rv1" "23132/87" "253J" "253J-BV"
For example, here we can use the cellInfo function to pull out the tissue of origin for each cell line in the CCLE dataset. The tissueid column is standard for PSets, and should be present in all objects.
mycol <- c("#8dd3c7","#ffffb3","#bebada","#fb8072","#80b1d3","#fdb462",
"#b3de69","#fccde5","#d9d9d9","#bc80bd","#ccebc5","#ffed6f",
"#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
"#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928")
pie(table(CCLE@cell[,"tissueid"]),
col=mycol,
main="Tissue types",
radius=1,
cex=0.8)
Figure 3: Tissue of origin of cell lines in CCLE study
The other important metadata slot will vary across the two packages, being called
drug in PharmacoGx, and radiation in RadioGx. They are discussed just below.
The CoreSet also includes annotations, which keep track of metadata about
the creation of the CoreSet object itself, and curation, which keeps track of the
remapping of identifiers from those used in the original study to those in the object.
Molecular data are contained in the molecularProfiles slot.
# Get the list (equivalent to @molecularProfiles, except that it is robust to changes in CoreSet structure
## PSet
str(molecularProfilesSlot(CCLE), max.level=2)
## List of 1
## $ rna:Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
## RSet
str(molecularProfilesSlot(Cleveland), max.level=2)
## List of 4
## $ rna :Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
## $ rnaseq :Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
## $ mutation:Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
## $ cnv :Formal class 'SummarizedExperiment' [package "SummarizedExperiment"] with 5 slots
# Get the names from the list
mDataNames(CCLE)
## [1] "rna"
mDataNames(Cleveland)
## [1] "rna" "rnaseq" "mutation" "cnv"
All molecular data in any class inheriting from CoreSet is contained in a SummarizedExperiment
object. While SummarizedExperiment comes with it’s own set of accessors, we recommend using
available RadioGx and PharmacoGx accessor methods as it allows your scripts to be robust to future changes
in the structure of a CoreSet object. Below, we demonstrate each method with an RSet, but encourage you to
try them on the PSets as well. They should all work!
# Get sample metadata
phenoInf <- phenoInfo(Cleveland, 'cnv')
To keep the document formatted nicely, the following tables have been subset to the first three rows and columns.
| Sample_title | Sample_geo_accession | Sample_status |
|---|---|---|
| A2780 | GSM887918 | Public on Mar 20 2012 |
| OVCAR-4 | GSM888565 | Public on Mar 20 2012 |
| VMRC-RCZ | GSM888829 | Public on Mar 20 2012 |
# Get feature metadata
featInfo <- featureInfo(Cleveland, 'rna')
| Probe | EnsemblGeneId | EntrezGeneId |
|---|---|---|
| ENSG00000000003_at | ENSG00000000003 | 7105 |
| ENSG00000000005_at | ENSG00000000005 | 64102 |
| ENSG00000000419_at | ENSG00000000419 | 8813 |
# Access the moleclar feature data
mProf <- molecularProfiles(Cleveland, 'rnaseq')
| G20461.HSC-3.2 | G20463.C2BBe1.2 | G20466.5637.2 | |
|---|---|---|---|
| ENSG00000000003 | 4.527 | 5.860 | 5.394 |
| ENSG00000000005 | 0.000 | 0.070 | 0.000 |
| ENSG00000000419 | 6.314 | 7.303 | 6.140 |
Data for treatment response can also be accesssed the same way across PharmacoGx and RadioGx.
The difference between the two packages is in the data itself, and the models applied to the data, discussed
below. Here is a quick summary of accessor functions for response data.
# Get sensitivity slot
sens <- sensitivitySlot(CCLE)
names(sens)
## [1] "info" "raw" "profiles" "n"
# Get sensitivity raw data
sensRaw <- sensitivityRaw(CCLE)
# Get sensitivity raw data
knitr::kable(sensRaw[,,2][1:3, 1:3])
| doses1 | doses2 | doses3 | |
|---|---|---|---|
| drugid_AEW541_1321N1 | 108.67 | 111.00 | 102.16 |
| drugid_AEW541_22Rv1 | 100.94 | 112.50 | 86.00 |
| drugid_AEW541_42-MG-BA | 108.91 | 108.39 | 96.50 |
# Get sensitivity profiles
sensProf <- sensitivityProfiles(CCLE)
knitr::kable(sensProf[1:3, 1:3])
| ic50_published | aac_published | amax_published | |
|---|---|---|---|
| drugid_AEW541_1321N1 | 8.000000 | 0.0873750 | -42.55801 |
| drugid_AEW541_22Rv1 | 2.329924 | 0.2205000 | -71.58934 |
| drugid_AEW541_42-MG-BA | 2.682130 | 0.1144375 | -63.49137 |
# Get sensitivity info
sensInfo <- sensitivityInfo(CCLE)
knitr::kable(sensInfo[1:3, 1:3])
| cellid | drugid | nbr.conc.tested | |
|---|---|---|---|
| drugid_AEW541_1321N1 | 1321N1 | Nvp-aew541 | 8 |
| drugid_AEW541_22Rv1 | 22Rv1 | Nvp-aew541 | 8 |
| drugid_AEW541_42-MG-BA | 42-MG-BA | Nvp-aew541 | 8 |
In PharmacoGx, metadata about the compounds used can be accessed through the drugInfo accessor.
This works very similar to the cellInfo function.
knitr::kable(drugInfo(CCLE)[1:3,1:3])
| Compound..code.or.generic.name. | Compound..brand.name. | Target.s. | |
|---|---|---|---|
| Crizotinib | PF-2341066 | Crizotinib | c-MET, ALK |
| Dovitinib | TKI258 | Dovitinib | EGFR, FGFR1, PDGFRbeta, VEGFR-1, KDR |
| Erlotinib | Erlotinib | Tarceva | EGFR |
Drug names are also accessed as one would expect:
drugNames(CCLE)
## [1] "Crizotinib" "Dovitinib" "Erlotinib" "Irinotecan"
## [5] "L-685458" "Lapatinib" "LBW242" "Nilotinib"
## [9] "Nutlin-3" "Nvp-aew541" "Nvp-tae 684" "Paclitaxel"
## [13] "Palbociclib" "Panobinostat" "PD-0325901" "Pha-665752"
## [17] "PLX4720" "Raf265(chir-265)" "Saracatinib" "Selumetinib"
## [21] "Sorafenib" "Tanespimycin" "Topotecan" "Vandetanib"
A unique slot to RadioGx, radiation has additional
accessor methods to retrieve the radiation types used in a given sensitivity experiment.
# Get the radiation info data.frame from an RSet
radInf <- radiationInfo(Cleveland)
knitr::kable(radInf)
| X.radiation. | |
|---|---|
| radiation | radiation |
Currently, only one type of radiation has been used in an RSet. However, we hope to add new RSets covering a wider range of radiation
sensitivity and perturbation experiments in the near future. The following method is also available to retrieve the radiation
types as a character vector instead of a data.frame.
radTypes <- radiationTypes(Cleveland)
radTypes
## [1] "radiation"
PSets can be subsetted by refering directly to the drugs and cell lines you want to keep in the dataset. For example, we can subset the GDSC PSet in the following ways:
print(GDSC)
## Name: GDSC_v1
## Date Created: Fri Jun 5 20:29:32 2020
## Number of cell lines: 1064
## RNA:
## Dim: 5947 789
## Drug pertubation:
## Please look at pertNumber(cSet) to determine number of experiments for each drug-cell combination.
## Drug sensitivity:
## Number of Experiments: 6890
## Please look at sensNumber(cSet) to determine number of experiments for each drug-cell combination.
print(drugNames(GDSC)[1])
## [1] "Paclitaxel"
print(subsetTo(GDSC, drugs="Erlotinib"))
## Name: GDSC_v1
## Date Created: Fri Jun 5 20:29:32 2020
## Number of cell lines: 824
## RNA:
## Dim: 5947 789
## Drug pertubation:
## Please look at pertNumber(cSet) to determine number of experiments for each drug-cell combination.
## Drug sensitivity:
## Number of Experiments: 511
## Please look at sensNumber(cSet) to determine number of experiments for each drug-cell combination.
print(GDSC["YT","Erlotinib"])
## Name: GDSC_v1
## Date Created: Fri Jun 5 20:29:32 2020
## Number of cell lines: 1
## RNA:
## Dim: 5947 0
## Drug pertubation:
## Please look at pertNumber(cSet) to determine number of experiments for each drug-cell combination.
## Drug sensitivity:
## Number of Experiments: 1
## Please look at sensNumber(cSet) to determine number of experiments for each drug-cell combination.
As an exercise, lets manually compute the number of overlaping cell lines between CCLE and GDSC. Try it yourself!
If you noticed as you completed the previous exercise, cell line and drug names are standardized between PSets downloaded using the downloadPSet function. This is the major benefit in working with the pre-curated objects: entities follow a standardized naming scheme, which was chosen to be human readable whenever possible.
PharmacoGx also implements a convenience function to intersect PSets:
common <- intersectPSet(list(CCLE, GDSC), intersectOn=c("cell.lines"))
print(common)
The structure of Xeva is different from the CoreGx structure. As such, many of the accessor functions are also different.
md.info <- modelInfo(brca)
head(md.info)
## model.id tissue tissue.name patient.id
## X.1004.BG98 X.1004.BG98 BRCA Breast Cancer X-1004
## X.1004.biib X.1004.biib BRCA Breast Cancer X-1004
## X.1004.BK20 X.1004.BK20 BRCA Breast Cancer X-1004
## X.1004.BY19 X.1004.BY19 BRCA Breast Cancer X-1004
## X.1004.BY19.LE11 X.1004.BY19.LE11 BRCA Breast Cancer X-1004
## X.1004.BY19.LJ16 X.1004.BY19.LJ16 BRCA Breast Cancer X-1004
## drug
## X.1004.BG98 BGJ398
## X.1004.biib binimetinib
## X.1004.BK20 BKM120
## X.1004.BY19 BYL719
## X.1004.BY19.LE11 BYL719 + LEE011
## X.1004.BY19.LJ16 BYL719 + LJM716
Drug-Dose response data included in the PharmacoSet objects can be conviniently plotted using the drugDoseResponseCurve function. Given a list of PharmacoSets, a drug name and a cell name, it will plot the drug dose response curves for the given cell-drug combination in each dataset, allowing direct comparisons of data between datasets.
cells <- c("SK-MEL-2","697","NCI-H1666")
# par(mfrow=c(1, 3), pty = "s")
drugDoseResponseCurve(drug="Lapatinib", cellline=cells[1],
pSets=list(CCLE, GDSC), plot.type="Actual",
legends.label="aac_published")
drugDoseResponseCurve(drug="Lapatinib", cellline=cells[2],
pSets=list(CCLE, GDSC), plot.type="Actual",
legends.label="aac_published")
drugDoseResponseCurve(drug="Lapatinib", cellline=cells[3],
pSets=list(CCLE, GDSC), plot.type="Actual",
legends.label="aac_published")
The function drugDoseResponseCurve can also be used to plot your own drug dose response curves, as follows:
concentrations <- 1/2^seq(0,8) * 1
viabilities <- c(0, 33.3, 60, 77.8, 88.2, 93.9, 96.9, 98.4, 99.2)
drugDoseResponseCurve(concentrations = list("Exp 1" = concentrations), viabilities = list("Exp 1" = viabilities))
## Warning in sanitizeInput(concentrations[[i]], viabilities[[i]], conc_as_log =
## conc_as_log, : Concentration Values were unsorted. Sorting concentration and
## ordering viability in same order
One of the core tasks implemented in PharmacoGx is the fitting of Hill Curve Models to dose-response data. In PharmacoGx, we use the 3 Parameter Hill Curve function as our model of drug response in cancer cell lines:
\[y = E_\infty + \frac{1-E_\infty}{1 + (\frac{x}{EC50})^{HS}}\] This is a log-logistic model, meaning that it takes the form of a logistic curve when x is on the log scale. The three parameters are: \(E_\infty\), which is the maximal inhibition predicted at infinite concetration of the drug (the right asymptote); \(EC50\), which is the inflection point of the logit, where the inhibition reaches 50% of the maximum; \(HS\), the Hill Slope, which is a measure of the cooperativity of binding. This parameter controls the steepness of the logit, and is interpreted based on its relation to 1. Values larger than 1 imply positive cooperativity in inhibition/binding of the target for the small molcule ligands, which values less than 1 imply negative cooperativity/antagonism.
To fit a Hill Slope model to your data in PharmacoGx, you can use the logLogisticRegression function, as below:
concentrations <- 1/2^seq(0,8) * 1
viabilities <- c(0, 33.3, 60, 77.8, 88.2, 93.9, 96.9, 98.4, 99.2)
pars <- logLogisticRegression(conc = concentrations, viability = viabilities)
## Warning in sanitizeInput(conc = conc, viability = viability, conc_as_log =
## conc_as_log, : Concentration Values were unsorted. Sorting concentration and
## ordering viability in same order
print(pars)
## $HS
## [1] 1.900066
##
## $E_inf
## [1] 0
##
## $EC50
## [1] 0.2813364
For all the data in PSets, this modeling has been precomputed and stored in the object,
under sensitivityProfiles:
knitr::kable(sensitivityProfiles(CCLE)[1:5,c("E_inf", "EC50", "HS")])
| E_inf | EC50 | HS | |
|---|---|---|---|
| drugid_AEW541_1321N1 | 38.47711 | 3.295213 | 0.9169196 |
| drugid_AEW541_22Rv1 | 13.95620 | 1.376470 | 0.8374151 |
| drugid_AEW541_42-MG-BA | 36.28378 | 1.723197 | 2.9099240 |
| drugid_AEW541_5637 | 0.00000 | 5.192732 | 1.0883419 |
| drugid_AEW541_639-V | 30.29307 | 1.350122 | 0.6991911 |
Often with in vitro Pharmacogenomics data, we want to compare the drug sensitivity of a cell line to some omic feature. For this, we want to summarize the drug dose response curve into a single number representing the sensitivity of the cell line. The IC50 and Area Above the Curve are two convenient metrics for quantifying the observed drug sensitivity. If you noticed above, the drugDoseResponseCurve function computes them by default. In PharmacoGx, they can be computed manually as follows:
concentrations <- rev(1/2^seq(0,8) * 1)
viabilities <- c(99.2, 98.4, 96.9, 93.9, 88.2, 77.8, 60, 33.3, 0)
print(PharmacoGx::computeAUC(concentration = concentrations, viability = viabilities))
## [1] 23.68416
print(PharmacoGx::computeIC50(concentration = concentrations, viability = viabilities))
## [1] 0.2813364
In PharmacoGx, we call these measures sensitivity measures. PSets come with these measures precomputed, and accessible using the sensitivityProfiles function:
knitr::kable(head(sensitivityProfiles(CCLE)[,1:5]))
| ic50_published | aac_published | amax_published | aac_recomputed | ic50_recomputed | |
|---|---|---|---|---|---|
| drugid_AEW541_1321N1 | 8.000000 | 0.0873750 | -42.55801 | 0.0980082 | 16.332263 |
| drugid_AEW541_22Rv1 | 2.329924 | 0.2205000 | -71.58934 | 0.2132382 | 2.034710 |
| drugid_AEW541_42-MG-BA | 2.682130 | 0.1144375 | -63.49137 | 0.1215116 | 2.687672 |
| drugid_AEW541_5637 | 5.002314 | 0.1243550 | -62.35278 | 0.1087770 | 5.192732 |
| drugid_AEW541_639-V | 1.736181 | 0.1936250 | -51.95981 | 0.1834458 | 5.113119 |
| drugid_AEW541_697 | 4.260822 | 0.1087625 | -73.33379 | 0.1051855 | 4.254253 |
RadioGx provides a number of functions for analyzing dose response experiments. To use these functions, we must first fit a statistical model to the dose response data. Due to the nature of radiation treatment, the model used is different from the model in Pharmacogenomics. While Pharmacogenomics uses a log-logistic Hill Curve for modeling the dose-response relationship, Radiogenomics analysis traditionally uses a Linear Quadratic model. This is due to the different action of radiation treatment compared to compound inhibition of targets.
This package exports a function for fitting linear-quadratic models to dose response data. The function can be used with data contained in a RadioSet or with raw dose-response data.
# Extract raw sensitvity data from the RadioSet
sensRaw <- sensitivityRaw(Cleveland)
str(sensRaw)
## num [1:305, 1:9, 1:2] 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "dimnames")=List of 3
## ..$ : chr [1:305] "SK-N-FI_radiation_1" "SK-N-AS_radiation_3" "CHP-212_radiation_4" "MHH-NB-11_radiation_6" ...
## ..$ : chr [1:9] "doses1" "doses2" "doses3" "doses4" ...
## ..$ : chr [1:2] "Dose" "Viability"
The data returned by sensitivityRaw(RSet) is a three dimensional
array, but it can also be thought of as a set of experiment by treatment
matrices. We can see by the dimnames of the third dimensions that
the first matrix holds the radiation dose (in Gy) for each experiment,
while the second matrix holds the viability measurements for the cell-line
after each dose in the experimental series.
# Find a cancer cell-line of interest
head(sensitivityInfo(Cleveland)$cellid)
## [1] "SK-N-FI" "SK-N-AS" "CHP-212" "MHH-NB-11" "SK-N-SH" "NB1"
cancerCellLine <- sensitivityInfo(Cleveland)$cellid[1]
print(cancerCellLine)
## [1] "SK-N-FI"
# Get the radiation doses and associated survival data from clevelandSmall
radiationDoses <- sensRaw[1, , 'Dose']
survivalFractions <- sensRaw[1, , 'Viability']
LQmodel <- linearQuadraticModel(D=radiationDoses,
SF=survivalFractions)
LQmodel
## alpha beta
## 0.15219719 0.01783452
## attr(,"Rsquare")
## [1] 0.9882769
Above we see that LQmodel contains the alpha and beta coefficients for the dose response curve fit to the dose and viability data for the IMR-32 cancer cell-line. Based on the \(R^2\) attribute we can see that the model fit for this data is good, with 98.8% of observed variance explained by the model.
RadioGx provides a number of functions for calculating common dose response metrics such as surviving fraction (SF), area under the curve (AUC) and dose at which only 10% of cancer cells survive (D10).
Some of these functions require the alpha and beta coefficients, as calculated
above using the linearQuadraticModel function.
survFracAfter2Units <- computeSF2(pars=LQmodel)
print(survFracAfter2Units)
## [1] 0.6867861
dose10PercentSurv <- computeD10(pars=LQmodel)
print(dose10PercentSurv)
## [1] 7.870413
We see from the above code cell that after two units of radiation,
68.679% of cancer cells remain relative to the initial population.
Conversely, using computeD10 we see that on average 7.87 units of radiation
need to be administered to result in 10% cell-line survival (i.e., 90% of cancer cells
are killed).
Other dose-response metrics can be computed directly using radiation dose and cancer cell viability data.
areaUnderDoseRespCurve <- computeAUC(D=radiationDoses, pars=LQmodel, lower=0,
upper=1)
print(areaUnderDoseRespCurve)
## [1] 2.872288
In the above code block we compute the AUC for a dose-response curve between a dose of 0 to 1 Gy. This area can be interpreted as the total proportion of cells killed during the administration of 1 Gy of radiation.
The doseResponseCurve function can be used to generate plots of surviving
fraction vs dose for radiation sensitivity experiments. In this example
we provide raw data values to create the plot. When the plot.type is set to
“Both”, a linear-quadratic model will also be fit to the supplied dose-response
values.
doseResponseCurve(
Ds=list("Experiment 1" = c(0, 2, 4, 6)),
SFs=list("Experiment 1" = c(1,.6,.4,.2)),
plot.type="Both"
)
Additionally, doseResponseCurve can be used to create dose response curves
directly from a curated RadioSet object. When utilizing this feature, a
cell-line must be selected from the RadioSet. This can be done by name
if you know which cell-line you are looking for. If you don’t know which
cell-line you want to visualize, the available
cell-lines can be explored using the cellInfo function.
doseResponseCurve(
rSets=list(Cleveland),
cellline=cellInfo(Cleveland)$cellid[3]
)
Drug response in PDX models is usually not assessed in the same way as in vitro. Rather than looking at viability vs dose curves, one looks at tumour growth vs time curves, to see if the treatement with a drug can inhibit tumour growth or initiate tumour shrinkage and clearance. Given the increased complexity of a tumour growing within a living mouse, there is no canonical model that is fit to this data. Rather, there are several options for assessing response in these models, each with its pros and cons.
Xeva provides functions to visualize these PDX time vs. tumor volume curve.
plotPDX(object = brca, patient.id = "X-1004", drug = "BGJ398")
plotPDX(object=brca,patient.id="X-1004",drug="BGJ398",control.name="untreated")
We can also plot the normalized volume.
plotPDX(object=brca,patient.id="X-1004",drug="BGJ398",control.name="untreated",
vol.normal = TRUE)
When replicates are avaliable Xeva can be used the visualize each model.
data("repdx")
plotPDX(object=repdx,patient.id="P1",drug="treatment",control.name="Control",
vol.normal = TRUE)
In Xeva several response computing functions such as mRECIST, slope, AUC etc. have been implemented. Drug response can be computed for PDX models as:
response(brca, model.id="X.1004.BG98", res.measure="mRECIST")
## computing mRECIST for X.1004.BG98
## $volume.change
## [1] 0.00000 -8.91337 -13.52028 -35.10265 -54.28142 -41.36204 -38.55784
## [8] -32.69905 -28.24236
##
## $average.response
## [1] 0.000000 -4.456685 -7.477883 -14.384076 -22.363545 -25.529962 -27.391087
## [8] -28.054582 -28.075447
##
## $best.response
## [1] -54.28142
##
## $best.response.time
## [1] 12
##
## $best.average.response
## [1] -28.07545
##
## $best.average.response.time
## [1] 26
##
## $mRECIST
## [1] "PR"
Drug response for all models is stored in the sensitivity slot and can be extracted using
brca.mr <- summarizeResponse(brca, response.measure = "mRECIST",
group.by="patient.id")
We can visualize this matrix as:
plotmRECIST(brca.mr, control.name = "untreated")
Pharmacogenomics studies often contain many examples of either replicated or missing data. One of the most common tasks in preparing data for statistical or machine learning analysis is aligning your features and labels. To accelerate using Pharmacogenomics data for analysis, PharmacoGx contains two functions which create deduplicated matrices with missing data filled by NAs: summarizeSensitivityProfiles and summarizeMolecularProfiles. They create matrices which are drugs x cell lines and molecular features x cell lines, with any cell lines profiled for only sensitivity or molecular features padded with NA values. For the molecular profiles, the data are returned in a SummarizedExperiment object, while drug sensitivity data are returned as a matrix.
mol.prof <- PharmacoGx::summarizeMolecularProfiles(CCLE, "rna")
sens.prof <- PharmacoGx::summarizeSensitivityProfiles(CCLE, "aac_recomputed")
dim(mol.prof)
## [1] 10012 1094
dim(sens.prof)
## [1] 24 1094
Below, we use the summarizeSensitivityProfiles function and ggplot to investigate the distributions of AUC values within CCLE.
library(ggplot2, verbose=FALSE)
library(reshape2, verbose=FALSE)
CCLE.aac <- PharmacoGx::summarizeSensitivityProfiles(CCLE, sensitivity.measure = "aac_recomputed")
melted_data <- melt(CCLE.aac)
NA_rows <- unique(which(is.na(melted_data), arr.ind=T)[,1])
melted_data <- melted_data[-NA_rows,]
p <- ggplot(melted_data, aes(x=Var1,y=value)) +
geom_boxplot(fill="gray") +
theme(axis.text.x=element_text(angle=90,hjust=1)) +
xlab("Drugs") +
ylab("AAC")
print(p)
Figure 4: Cells response to drugs in CCLE
To retrieve a radiation type by cell-line summary of a sensitivity experiment, we use the summarizeSensitivityProfiles function.
This will return a matrix where rows are radiation type3 Note there is currently only one radiation type available, but in the future there will be more, columns are cell-line and values are viability measurements summarized
using summary.stat4 Available options are ‘mean’, ‘median’, ‘first’, ‘last’. The sensitivity measure to summarize can be
specified using
sensitivity.measure5 See sensitivityMeasures(RSet) for available options.
sensSummary <- RadioGx::summarizeSensitivityProfiles(Cleveland)
sensSummary[, 1:3]
## SK-N-FI SK-N-AS CHP-212
## 2.861642 3.258931 1.344093
mprofSummary <- RadioGx::summarizeMolecularProfiles(Cleveland, mDataType='rna', summary.stat='median', fill.missing=FALSE)
mprofSummary
## class: SummarizedExperiment
## dim: 20049 292
## metadata(3): experimentData annotation protocolData
## assays(2): exprs se.exprs
## rownames(20049): ENSG00000000003 ENSG00000000005 ... ENSG00000280439
## ENSG00000280448
## rowData names(7): Probe EnsemblGeneId ... BEST rownames
## colnames(292): SK-N-FI SK-N-AS ... VM-CUB-1 T.T
## colData names(25): samplename filename ... rownames tissueid
Due to a lack of replicates in the Cleveland RSet, the returned SummarizedExperiment object contains the same information as
the original. For other experiments with replicates, however, the result should contain one column per unique cell-line id. For
ease of interoperability with the response data contained in an RSet, if fill.missing is FALSE empty columns for the
cell-lines in the sensitivity experiment, but not in the molecular profile will be added to the SummarizedExperiment such
that the dimensions are equal.
As already seen above, Xeva provides a method to extract drug response data from XevaSets. Like PharmacoGx and RadioGx, the package also provides a method to extract the molecular data associated with a particular set of mice. For example, we can extract the RNAseq data for all the PDXs treated with trastuzumab, as below:
brca_trastuz <- Xeva::summarizeMolecularProfiles(brca, mDataType="RNASeq", drug = "trastuzumab")
Finally, we will talk about some functions built into PharmacoGx to generate signatures of molecular features which
correlate with response to particular compounds. In PharmacoGx, the main function to generate signatures of response
across drugs is drugSensitivitySig. It takes as input a PSet, and the molecular data type you wish to use to
generate the signatures, and computes the linear association between each feature and each drug response vector independently.
The default of using all features, drugs and cell lines in the dataset can be modified with parameters detailed below.
At its core, drugSensitivitySig fits a nested linear model to the drug response data, using the molecular feature, as well as cell tissue of origin and batch effect as possible predictors. The function then uses ANOVA to judge whether including the molecular feature in the model leads to a statistically significant improvement in prediction accuracy (R^2). For more details on the implementation, we encourage readers to reference the PharmacoGx manuscript, or the vignettes included in the package.
Here, as an example, we will run drugSensitivitySig to generate a signature of response to Lapatinib and Erlotinib, two
targetted therapies, from the GDSC data. We will do this with a random 10 genes measured in the
expression matrix, as the full computation can take some time to complete.
## Extracting the first 100 gene names. featureInfo brings up the rowData of the "rna" SummarizedExperiment
chosen.genes <- sample(rownames(featureInfo(GDSC, "rna"), 10))
sigs <- PharmacoGx::drugSensitivitySig(GDSC, "rna", sensitivity.measure = "aac_recomputed", drugs=c("Erlotinib", "Lapatinib"), features = chosen.genes)
Lets look at the head of the signature for the two drugs:
knitr::kable(x = head(sigs[,1,]))
| estimate | se | n | tstat | fstat | pvalue | df | fdr | |
|---|---|---|---|---|---|---|---|---|
| ENSG00000177311 | -0.0019951 | 0.0556976 | 288 | -0.0358199 | 0.0012831 | 0.9714532 | 262 | 0.9928222 |
| ENSG00000162512 | -0.0389686 | 0.0666560 | 288 | -0.5846228 | 0.3417838 | 0.5593044 | 262 | 0.8840945 |
| AFFX-BioB-5 | 0.0563077 | 0.0539462 | 288 | 1.0437753 | 1.0894668 | 0.2975515 | 262 | 0.7657026 |
| ENSG00000118193 | 0.0436027 | 0.0579524 | 288 | 0.7523887 | 0.5660888 | 0.4524927 | 262 | 0.8349730 |
| ENSG00000137500 | 0.0427207 | 0.0587005 | 288 | 0.7277741 | 0.5296552 | 0.4674017 | 262 | 0.8428254 |
| ENSG00000127666 | 0.0643834 | 0.0567248 | 288 | 1.1350134 | 1.2882554 | 0.2574070 | 262 | 0.7424583 |
knitr::kable(x = head(as.data.frame(sigs[,2,])))
| estimate | se | n | tstat | fstat | pvalue | df | fdr | |
|---|---|---|---|---|---|---|---|---|
| ENSG00000177311 | -0.0733415 | 0.0563583 | 297 | -1.3013430 | 1.6934935 | 0.1942463 | 271 | 0.6591102 |
| ENSG00000162512 | -0.1381964 | 0.0676148 | 297 | -2.0438777 | 4.1774360 | 0.0419323 | 271 | 0.4135514 |
| AFFX-BioB-5 | -0.0080425 | 0.0550268 | 297 | -0.1461556 | 0.0213615 | 0.8839072 | 271 | 0.9723634 |
| ENSG00000118193 | -0.0659897 | 0.0579688 | 297 | -1.1383647 | 1.2958742 | 0.2559736 | 271 | 0.7067200 |
| ENSG00000137500 | 0.0817634 | 0.0569095 | 297 | 1.4367282 | 2.0641881 | 0.1519487 | 271 | 0.6127403 |
| ENSG00000127666 | -0.0272001 | 0.0578711 | 297 | -0.4700115 | 0.2209108 | 0.6387248 | 271 | 0.9074887 |
The true usefulness of the RadioGx packages comes from the ability to determine gene signatures for a
cell-lines from a sensitivity experiment. Cell-lines of interest to a given researcher can be selected
and a molecular signature computed which correlates specific molecular features with a given sensitivity profile.
Using this method one could identify signatures associated with either radio-sensitivity or radio-resistance.
Combining this signature with drug response gene signatures from PharmacoGx, as will
be done in the subsequent section, one can identify drugs which could augment the effectiveness of a
given radiation signature. Perhaps more powerfully, drugs which target features associated with radio-resistance
can be found, potentially synergistically increasing the overall effectiveness of the combined treatment. We must
note however that this is an active area of research, and the examples provided are illustrative only. When conducting
research in this field, care must be taken in choosing the right model systems, measures of sensitivity, and
incorporating prior knowledge to differentiate promising hits from experimental artifacts.
radSensSig <- radSensitivitySig(Cleveland, mDataType='rna', nthread=2)
## Computing radiation sensitivity signatures...
# Convert to a data.frame
radSensSigDF <- data.frame(radSensSig@.Data[, , ], row.names=rownames(radSensSig))
# Order by estimated correlation
radSensSigDF <- radSensSigDF[order(radSensSigDF$estimate, decreasing=TRUE), ]
# Subset to only significant FDR
radSensSigDF <- radSensSigDF[radSensSigDF$fdr < 0.05, ]
radSensGeneSig <- head(radSensSigDF, 10)
radResistGeneSig <- tail(radSensSigDF[radSensSigDF$fdr < 0.05, ], 10)
knitr::kable(radSensGeneSig, caption="Top 10 Gene Markers of Radio-sensitivity in the Cleveland RSet")
| estimate | se | n | tstat | fstat | pvalue | df | fdr | |
|---|---|---|---|---|---|---|---|---|
| ENSG00000151117 | 0.3976407 | 0.0595970 | 292 | 6.672160 | 44.51772 | 0.00e+00 | 269 | 0.0000029 |
| ENSG00000132746 | 0.3523199 | 0.0708910 | 292 | 4.969883 | 24.69974 | 1.20e-06 | 269 | 0.0044207 |
| ENSG00000070731 | 0.3286902 | 0.0659831 | 292 | 4.981431 | 24.81465 | 1.10e-06 | 269 | 0.0044207 |
| ENSG00000124664 | 0.3165793 | 0.0771910 | 292 | 4.101246 | 16.82022 | 5.45e-05 | 269 | 0.0168071 |
| ENSG00000148513 | 0.3104869 | 0.0616191 | 292 | 5.038810 | 25.38960 | 9.00e-07 | 269 | 0.0044207 |
| ENSG00000183888 | 0.3100016 | 0.0647328 | 292 | 4.788945 | 22.93399 | 2.80e-06 | 269 | 0.0046427 |
| ENSG00000159763 | 0.3055805 | 0.0602806 | 292 | 5.069301 | 25.69781 | 7.00e-07 | 269 | 0.0044207 |
| ENSG00000110484 | 0.2972925 | 0.0613169 | 292 | 4.848461 | 23.50757 | 2.10e-06 | 269 | 0.0046427 |
| ENSG00000145817 | 0.2946001 | 0.0610280 | 292 | 4.827293 | 23.30276 | 2.30e-06 | 269 | 0.0046427 |
| ENSG00000110171 | 0.2937235 | 0.0659120 | 292 | 4.456296 | 19.85857 | 1.22e-05 | 269 | 0.0097822 |
knitr::kable(radResistGeneSig, caption="Top 10 Gene Markers of Radio-resistance in the Cleveland RSet")
| estimate | se | n | tstat | fstat | pvalue | df | fdr | |
|---|---|---|---|---|---|---|---|---|
| ENSG00000106346 | -0.2650079 | 0.0583550 | 292 | -4.541304 | 20.62344 | 8.40e-06 | 269 | 0.0080624 |
| ENSG00000213160 | -0.2652584 | 0.0620200 | 292 | -4.276979 | 18.29255 | 2.63e-05 | 269 | 0.0132056 |
| ENSG00000129351 | -0.2659001 | 0.0564573 | 292 | -4.709758 | 22.18182 | 4.00e-06 | 269 | 0.0056971 |
| ENSG00000056277 | -0.2680220 | 0.0584539 | 292 | -4.585184 | 21.02392 | 7.00e-06 | 269 | 0.0077473 |
| ENSG00000164167 | -0.2740782 | 0.0568968 | 292 | -4.817111 | 23.20455 | 2.40e-06 | 269 | 0.0046427 |
| ENSG00000105255 | -0.2816045 | 0.0680826 | 292 | -4.136217 | 17.10829 | 4.72e-05 | 269 | 0.0158575 |
| ENSG00000101974 | -0.2837507 | 0.0573454 | 292 | -4.948095 | 24.48364 | 1.30e-06 | 269 | 0.0044207 |
| ENSG00000254726 | -0.2863624 | 0.0605707 | 292 | -4.727737 | 22.35150 | 3.70e-06 | 269 | 0.0056547 |
| ENSG00000189007 | -0.2894647 | 0.0625417 | 292 | -4.628346 | 21.42158 | 5.70e-06 | 269 | 0.0072435 |
| ENSG00000166582 | -0.3080795 | 0.0642025 | 292 | -4.798563 | 23.02620 | 2.70e-06 | 269 | 0.0046427 |
For in vivo biomarker discovery we need the genomic data along with drug response. As discussed above, this can be extracted using the summarizeMolecularProfiles function. The output will be an ExpressionSet object which contains genomic data and drug response.
suppressMessages(library(Biobase))
dr.mol = summarizeMolecularProfiles(brca, drug="BGJ398", mDataType="RNASeq",
tissue= "BRCA",
sensitivity.measure="best.average.response")
exprs(dr.mol)[1:5, 1:3]
## X.1004.BG98 X.1008.BG98 X.1298.BG98
## PIK3R6 0.00000000 0.1505597 0.000000
## FGFR1 2.01077984 1.8718436 4.691534
## NTRK2 0.01435529 3.1110313 3.077243
## AKT1 6.65635335 7.4016475 6.700023
## FGFR4 2.78659636 2.5084287 5.132166
pData(dr.mol)[1:5, c("model.id", "best.average.response")]
## model.id best.average.response
## X.1004.BG98 X.1004.BG98 -28.07545
## X.1008.BG98 X.1008.BG98 14.56386
## X.1298.BG98 X.1298.BG98 13.79941
## X.1371.BG98 X.1371.BG98 49.50584
## X.1383.BG98 X.1383.BG98 10.26197
The output can be used for further inquiry such as correlation analysis.
genes <- rownames(exprs(dr.mol))
output <- data.frame()
for(g in genes)
{
cr <- cor.test(exprs(dr.mol)[g, ], pData(dr.mol)$best.average.response)
output <- rbind(output, data.frame(gene=g, cor=cr$estimate, p=cr$p.value))
}
print(output)
## gene cor p
## cor PIK3R6 -0.05894149 0.725207136
## cor1 FGFR1 -0.12639142 0.449563925
## cor2 NTRK2 -0.27175552 0.098844880
## cor3 AKT1 -0.03367497 0.840926277
## cor4 FGFR4 -0.03287113 0.844676600
## cor5 MAP2K2 -0.21939126 0.185691938
## cor6 TP53 -0.13546495 0.417414651
## cor7 IGF1R 0.20367636 0.220006862
## cor8 CDK13 -0.04403677 0.792920556
## cor9 MTOR -0.08916681 0.594475261
## cor10 MAP3K4 0.09801939 0.558238179
## cor11 CDK6 -0.16310369 0.327864479
## cor12 JAK1 0.13915034 0.404737205
## cor13 PIK3CA -0.03906225 0.815885006
## cor14 MDM2 -0.06212710 0.710978656
## cor15 PRLR -0.38563780 0.016796593
## cor16 ERBB2 -0.26781815 0.104025315
## cor17 PIK3C2G -0.19127852 0.249982554
## cor18 ERBB3 -0.46766443 0.003070459
## cor19 MAPKAPK2 -0.03310159 0.843601013
## cor20 ESR1 -0.14467750 0.386143613
## cor21 TUBB3 0.05966703 0.721958326
Below, we will use this functionality of Xeva to see if an association discovered in vitro holds up in an in vivo setting.
In this section, we will go through two short examples of analysis which integrates across the packages presented above, highlighting the use of these packages to explore hypotheses across domains.
Both RadioGx and PharmacoGx allow you to compute signatures of how much each molecular feature correlates with response to treatment, in the in vitro model systems. A natural question to ask is how the signature for response to gamma radiation will compare to signatures of response to various drugs. This can be used to generate hypothesis for combination therapies, or to gain insight into mechanisms of action.
We will run through the steps necessary to generate these results below, but note that the example datasets included with this workshop are incomplete (due to file size limits for github hosted R packages), and that due to time constraints the signatures we examine will have been precomputed from the full objects. We encourage the reader to work through this example with the full dataset.
To start, we will load the packages, and the data we will be using.
library(PharmacoGx)
library(RadioGx)
GDSC1 <- downloadPSet("GDSC_2019(v1_8.0)")
Cleveland <- downloadRSet("Cleveland")
In reality, we run the code below for the purpose of building this vignette:
data("GDSC")
data("Cleveland")
Now, we will compute signatures of radiation sensitivity and drug sensitivity for a selected set of drugs: Lapatinib, Dabrafenib, Paclitaxel, and Cisplatin. These drugs were chosen to have a mix of targetted and broad action therapies, and a couple of different targets for the two groups.
ft.rad <- rownames(featureInfo(Cleveland, "rna"))[which(featureInfo(Cleveland, "rna")$GeneBioType == "protein_coding")]
ft.info <- featureInfo(GDSC, "rna")
ft.drug <- rownames(ft.info)[which(ft.info$GeneBioType == "protein_coding")]
drugs.selected <- c("Lapatinib", "Paclitaxel", "Cisplatin", "Dabrafenib")
radSig <- radSensitivitySig(Cleveland, mDataType = "rna", features=ft.rad)
gdscSigs <- drugSensitivitySig(GDSC, mDataType="rna", sensitivity.measure="aac_recomputed", drugs = drugs.selected, features=ft.drug)
We will preload the precomputed signatures in interest of computation time.
data(gdscSigs)
data(radSig)
common.genes <- intersect(rownames(gdscSigs), rownames(radSig))
radSig <- radSig[common.genes,,,drop=FALSE]
gdscSigs <- gdscSigs[common.genes,,]
Now, we can go directly to comparing the signatures. For this, we will use a function called connectivityScore, which
wraps around two methods. It provides an interface to GSEA, for connecting your sensitivity signature to a disease or
pathway gene set. It also implements genome wide weighted correlation, allowing the assessment of signature similarity,
with the weights derived from significance estimates for the association of each gene with drug response.
connectivity.res <- lapply(drugs.selected, function(drug){
return(connectivityScore(x=radSig[,1,c("estimate", "pvalue")], y=gdscSigs[,drug, c("estimate","pvalue")], method = "gwc", nperm = 200))
})
names(connectivity.res) <- drugs.selected
print(connectivity.res)
## $Lapatinib
## score p
## 0.498671804 0.009950249
##
## $Paclitaxel
## score p
## -0.008382044 0.250000000
##
## $Cisplatin
## score p
## -0.510548599 0.009950249
##
## $Dabrafenib
## score p
## 0.069866152 0.009950249
library(PharmacoGx)
library(Xeva)
data("brca")
data("GRAY")
ERBB2 <- PharmacoGx::drugSensitivitySig(GRAY, mDataType="Kallisto_0.46.1.rnaseq", drug="Trastuzumab", sensitivity.measure="aac_recomputed", feature="ENSG00000141736.13")[1,1,]
## Summarizing Kallisto_0.46.1.rnaseq molecular data for: GRAY
##
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## Computing drug sensitivity signatures...
ERBB2.in.vivo <- drugSensitivitySig(brca, "trastuzumab", mDataType = "RNASeq", features=c("ESR1", "ERBB2"), sensitivity.measure = "best.average.response")
## Running for drug trastuzumab